Real-time drawing assistance through crowdsourcing
July 24, 2013
Researchers from Carnegie Mellon and Microsoft Research have proposed a new method for the large-scale collection and analysis of drawings by using a mobile game specifically designed to collect such data.
Analyzing this crowdsourced drawing database, the researchers build a spatially varying model of artistic consensus at the stroke level. They then present a surprisingly simple stroke-correction method which uses their artistic consensus model to improve strokes in real-time.
Importantly, the auto-corrections run interactively and appear nearly invisible to the user while seamlessly preserving artistic intent. Closing the loop, the game itself serves as a platform for large-scale evaluation of the effectiveness of the researchers’ stroke-correction algorithm.